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Fault early warning and recognition of power plant auxiliary equipment based on dynamic memory matrix and weighted MSET
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Xinggang YU1, Richeng WANG1, Jun ZENG2, Xin WEI3, Binbin QIU3
Thermal Power Generation | 2025, 54(3) : 140 - 149
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Thermal Power Generation | 2025, 54(3): 140-149
• Power generation technology forum •
Fault early warning and recognition of power plant auxiliary equipment based on dynamic memory matrix and weighted MSET
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Xinggang YU1, Richeng WANG1, Jun ZENG2, Xin WEI3, Binbin QIU3
Affiliations
  • 1.Hunan Province Key Laboratory of Efficient & Clean Power Generation Technologies, (State Grid Hunan Electric Power Corporation Limited Research Institute), Changsha 410017, China
  • 2.Hunan Xiangdian Test & Research Institute Co., Ltd., Changsha 410017, China
  • 3.State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Published: 2025-03-25 doi: 10.19666/j.rlfd.202405129
Outline
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It is of great significance to carry out health condition assessment and fault early warning of auxiliary equipment for safe operation of thermal power units in new power system. By taking the forced draft fan of a supercritical 660 MW thermal power unit as the research object, a method to construct dynamic memory matrix based on multiple characteristic parameters is proposed. The application shows that the proposed method can improve calculating speed of model effectively while ensuring the accuracy of calculated results. This work also presents a calculation method of weighted coefficients to modify the multivariate state estimation technique (MSET). The global similarity and parameter similarity indexes are introduced for fault early warning and recognition. An early fault warning model based on dynamic matrix and weighted MSET is utilized to simulate faults of forced draft fan. The results indicate that the weighted MSET model can not only improve the prediction accuracy of abnormal parameters under fault conditions effectively, but also reduce the influence of abnormal parameters on the predicted results of normal parameters. Consequently, the model proposed can realize both early warning of forced draft fan faults and recognition of abnormal parameters.

fault early warning and recognition  /  dynamic memory matrix  /  characteristic parameters  /  multivariate state estimation  /  weighted coefficients
Xinggang YU, Richeng WANG, Jun ZENG, Xin WEI, Binbin QIU. Fault early warning and recognition of power plant auxiliary equipment based on dynamic memory matrix and weighted MSET[J]. Thermal Power Generation, 2025 , 54 (3) : 140 -149 . DOI: 10.19666/j.rlfd.202405129
Funding
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  • National Key Research and Development Program(2022YFB4100700)
  • Science and Technology Project of Hunan Xiangdian Test & Research Institute Co., Ltd.(XDKY-2021-08)
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Year 2025 volume 54 Issue 3
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Article Info
doi: 10.19666/j.rlfd.202405129
  • Receive Date：2024-05-22
  • Online Date：2026-03-06
  • Published：2025-03-25
Article Data
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History
  • Received：2024-05-22
Funding
National Key Research and Development Program(2022YFB4100700)
Science and Technology Project of Hunan Xiangdian Test & Research Institute Co., Ltd.(XDKY-2021-08)
Affiliations
    1.Hunan Province Key Laboratory of Efficient & Clean Power Generation Technologies, (State Grid Hunan Electric Power Corporation Limited Research Institute), Changsha 410017, China
    2.Hunan Xiangdian Test & Research Institute Co., Ltd., Changsha 410017, China
    3.State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202405129
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